Automated speech recognition proxy system for natural language understanding

ABSTRACT

An interactive response system mixes HSR subsystems with ASR subsystems to facilitate overall capability of user interfaces. The system permits imperfect ASR subsystems to nonetheless relieve burden on HSR subsystems. An ASR proxy is used to implement an IVR system, and the proxy dynamically selects one or more recognizers from a language model and a human agent to recognize user input. Selection of the one or more recognizers is based on factors such as confidence thresholds of the ASRs and availability of human resources for HSRs.

RELATED APPLICATIONS

This application is a continuation of commonly owned co-pending U.S. patent application Ser. No. 14/958,833, entitled “Automated Speech Recognition Proxy System for Natural Language Understanding”, filed Dec. 3, 2015, which is a continuation of U.S. patent application Ser. No. 13/936,440, entitled “Automated Speech Recognition Proxy System for Natural Language Understanding”, filed Jul. 8, 2013, which is a continuation-in-part and claims priority under 35 USC § 120 to U.S. patent application Ser. No. 13/070,865, entitled “Automated Speech Recognition Proxy System For Natural Language Understanding,” filed Mar. 24, 2011, now U.S. Pat. No. 8,484,031, which is a continuation-in-part of U.S. patent application Ser. No. 12/985,174 entitled “Automated Speech Recognition System for Natural Language Understanding”, filed Jan. 5, 2011. The contents of the above-referenced applications are hereby incorporated by reference as if fully set forth herein.

FIELD OF INVENTION

This invention relates to the field of interactive response communication systems, and, more particularly to an interactive response communications system that selectively routes utterances to automated speech recognition (ASR) processor(s), human speech recognition (HSR) resource(s), or both ASR and HSR facilities.

BACKGROUND

Many companies interact with their customers via electronic means (most commonly via telephone, e-mail, and online text chat). Such electronic systems save the companies a large amount of money by limiting the number of customer service or support agents needed. These electronic systems, however, generally provide a less than satisfactory customer experience. The customer experience may be acceptable for simple transactions, but are frequently inconsistent or downright frustrating if the customer is not adept at talking to or interacting with a computer.

Such interactive response systems are well known in the art. For example, providing customer service via telephone using an interactive voice response (IVR) system is one such system. An example of customer service systems utilizing IVR technology is described in U.S. Pat. No. 6,411,686. An IVR system typically communicates with customers using a set of prerecorded phrases, responds to some spoken input and touch-tone signals, and can route or transfer calls. A drawback to such IVR systems is that they are normally built around a “menu” structure, which presents callers with just a few valid options at a time and require a narrow range of responses from callers.

Many of these IVR systems now incorporate speech recognition technology. An example of a system incorporating speech recognition technology is described in U.S. Pat. No. 6,499,013. The robustness of the speech recognition technology used by IVR systems vary, but often have a predetermined range of responses that they listen for and can understand, which limits the ability of the end user to interact with the system in everyday language. Therefore, the caller will often feel that they are being forced to speak to the system “as though they are talking to a computer.” Moreover, even when interacting with a system that utilizes speech recognition, customer input is often either not recognized or incorrectly determined, causing the customer to seek a connection to a human customer service agent as soon as possible.

Human customer service agents continue to be used for more involved customer service requests. These agents may speak to the customer over the phone, respond to customer e-mails, and chat with customers online. Agents normally answer customer questions or respond to customer requests. Companies have customer service groups, which are sometimes outsourced to businesses that specialize in “customer relations management.” Such businesses run centers staffed by hundreds of agents who spend their entire working day on the phone or otherwise interacting with customers. An example of such system is described in U.S. Pat. No. 5,987,116.

The typical model of customer service interaction is for one agent to assist a customer for the duration of the customer's interaction. At times, one agent (for example, a technical support representative) may transfer the customer to another agent (such as a sales representative) if the customer needs help with multiple requests. But in general, one agent spends his or her time assisting that one customer for the full duration of the customer's call or chat session, or is occupied resolving the customer's issue via e-mail. Most call centers also expect the agent to take the time to log (document) the call. Deficiencies in this heavy agent interface model is (1) there is a high agent turnover rate and (2) a great deal of initial and ongoing agent training is usually required, which all add up to making customer service a significant expense for these customer service providers.

In order to alleviate some of the expenses associated with agents, some organizations outsource their customer service needs. One trend in the United States in recent years, as high-speed fiber optic voice and data networks have proliferated, is to locate customer service centers overseas to take advantage of lower labor costs. Such outsourcing requires that the overseas customer service agents be fluent in English. In cases where these agents are used for telephone-based support, the agent's ability to understand and speak clearly in English is often an issue. An unfortunate result of off shore outsourcing is misunderstanding and a less than satisfactory customer service experience for the person seeking service.

Improved interactive response systems blend computer-implemented speech recognition with intermittent use of human agents. To some extent, this has been done for years; U.S. Pat. No. 5,033,088 addresses a system using both a human attendant and an automated speech recognizer. Likewise, U.S. Pat. No. 7,606,718 discloses a system in which a human agent is presented with only portions of a call requiring human interpretation of a user's utterance. The contents of these patents, as well as all other art referred to herein, is hereby incorporated by reference as is fully set forth herein. Interest in such systems is enhanced if they are relatively low in cost, which generally calls for limited human interaction. To achieve such limited human interaction, it would be desirable to have a system that required minimal initial training and for which results continued to improve over time. In particular, a learning/training system that provides “day-one” performance that is suitable for production use and that improves in efficiency quickly over time would be particularly valuable.

Many existing ASR systems suffer from serious training constraints such as the need to be trained to recognize the voice of each particular user of the system or the need to severely limit recognized vocabulary in order to provide reasonable results. Such systems are readily recognizable by users as being artificial. Consider the difference between the typical human prompt, “How can I help you?” and the artificial prompt, “Say MAKE if you want to make a reservation, STATUS if you would like to check on status of a reservation, or CANCEL to cancel a reservation.”

A goal of voice systems with ASR (Automated Speech Recognition) was to achieve a conversational system to perform caller interaction, much like HAL in “2001: A Space Odyssey”. To improve ASR capability, Voice User Interface (VUI) techniques have been developed to phrase prompts precisely and compactly in an attempt to reduce the vocabulary used and give the caller hints about the words they should speak to achieve higher accuracy speech recognition. Since then, ASR has improved and now addresses recognition of open-ended conversations. However, such open-ended conversations involve much larger vocabularies, resulting in much higher speech recognition error rates. The result is that callers are left with more frustration with and disdain for IVR systems based on, for instance, excessive confirmations of what was previously stated and understood, making incorrect choices, and forcing callers to back up to a previous menu. VUI designs attempt to lead the caller into what is known as a “directed dialog”, trying to narrow conversation from the general to the specific. Because small domains have a limited vocabulary and a significantly smaller repertoire of utterances, ASR and NLU have been more successful when applied to directed dialogs. The IVR industry is working to characterize knowledge domains using statistics and “search” with speech recognition to further increase understanding. However, these approaches still handle a significant number of callers poorly, especially those with dialects or pronunciation patterns that are difficult to understand even with sophisticated techniques such as building personalized ASR acoustic models. With the emergence of human-assisted recognition, there are now opportunities to leverage human understanding to recognize speech, text, graphics and video in conjunction with automation, making understanding more accurate and avoiding many of the weaknesses of ASR-based IVR systems. The fundamental task of IVR systems is to coordinate the filling of information slots in a range of business forms corresponding to user requests. In traditional IVR systems, this coordination is typically performed following a decision tree, fixed in advance, where there is little deviation from a restricted number of ways of interacting with users. Different kinds of recognition strategies have been developed, including variations in VUI design, different criteria that optimize for successful identification of accurate understanding, and techniques for understanding and recognition in the shortest possible time.

There are many reasons for a system to use a variety of appropriate techniques to make the interactions between a caller and automated system using human-assisted recognition as seamless and natural as possible.

Humans recognize and interpret meaning with much higher accuracy than Automated Speech Recognition (ASR), Graphics and Video Processing, and Natural Language Understanding (NLU) techniques. If humans can be used to understand when automation is insufficiently accurate, it now becomes possible to automate substantially more user interactions while still providing a good user experience. However, unlike computer resources, which can scale to meet unusual and unpredicted volume peaks, human resources need to be scheduled and may not be available in a timely manner for peaks. There is consequently a need for a system to automatically adjust to the required amount of HSR for any particular application, even using DTMF (dual-tone multi-frequency) when accuracy is not sufficient, to minimize the use of HSR. Even though the human interaction would change during unscheduled peaks, self-service could continue to be performed in a more traditional manner.

The traditional techniques used for tuning speech recognition and classifying recognized utterances to achieve the highest level of recognition change in subtle but important ways when the goal now becomes how to combine human-assisted and automation to best recognize and interpret the caller's utterances and at the same time achieve the most human-like user experience possible. Thus, a challenge not addressed by existing systems is how to use the most efficient combination of humans and automation in the given circumstances, under the given workload, while providing the most successful user experience.

Traditionally ASR systems start “listening” to utterances as they are spoken. If recognition automation fails, then the user would wait for the length of time that the complete utterances would take to be spoken before HSR would start listening and process it. It would be desirable if a system could attempt to understand the interaction in as close to real-time instead. For example, as the user speaks more and more words to describe their meaning (or “intent”), processing first by ASR and subsequently by HSR results in a significant time gap between the end of an utterance and the beginning of a response. This time gap could be filled, for example, with an audio play such as a typing sound. For some applications, this could be successful, especially for those applications that collect data. For other applications, this time gap makes it difficult to carry on a natural conversation with the system. In addition, longer speech also often results in lower recognition quality. Longer speech contains not only more words but also more word combinations. Taken together, these increase speech recognition errors and reduce understanding accuracy.

Therefore, an automated recognition system is needed that can understand as soon as possible to predict successful recognition prior to using human assistance to maintain human-like interactions. Furthermore, since human assistance may be called upon, this automated recognition system also needs the ability to monitor staffing of human assistance to adjust understanding confidence automatically and/or to go to complete automation depending on system status load and human assistance skill set capability.

Systems that are more ambitious, such as Natural Language Understanding (NLU) systems, require extensive machine learning periods of laborious hand-crafted grammar writing in order to get usable results from larger grammars and vocabularies. Particularly in environments in which vocabulary may be dynamic (such as a system to take ticket orders for a new play or for a concert by a new musical group), the learning period may be far too long to provide satisfactory results. Inclusion of accents, dialects, regional differences in vocabulary and grammar and the like further complicate the task of teaching such systems so that they can achieve reasonable thresholds of recognition accuracy.

ASR systems currently available are effective at recognizing simple spoken utterances such as numbers, data, and simple grammars (i.e., a small set of words and expressions made from them). However, to date ASR systems have not provided a high enough level of speech recognition performance to create a voice interface that provides a free-flowing conversation. Additionally, ASR performance degrades not only with accents and dialects as noted above, but also with background noise, adult rather than child voices, and, in many cases, female rather than male voices. ASR performance is improving over time, with some systems using statistical language models intended to recognize an extremely wide range of responses from callers, so that callers can be recognized even when they speak naturally rather than in a highly constrained manner. Even so, ASR performance has not yet rivaled actual interaction between humans, and the ASR systems that provide the highest levels of performance are time consuming and expensive to build and to tune for specific applications.

Tuning of grammars by considering statistical probabilities of various expected answers, as well as synonyms, is one technique used to improve ASR performance. Another is development of statistical language models, which can involve significant efforts to transcribe recordings of utterances of live phone conversations with live operators. ASR performance is quite acceptable in certain applications but is not yet suitable for others, so known ASR-based systems continue to lack the capability to understand natural unconstrained utterances.

Therefore, there remains a need in the art for an interactive system that provides a consistently high-quality experience without the limitations of constituent ASR components.

SUMMARY

An interactive response system mixes HSR subsystems with ASR subsystems to facilitate natural language understanding and improve overall capability of voice user interfaces. The system permits imperfect ASR subsystems to use HSR when necessary, and to nonetheless relieve the burden on HSR subsystems under load. An ASR proxy is used to implement an IVR system, and the proxy decides, based on a set of rules, to route an utterance solely to one ASR, to route it to an HSR in addition to at least one ASR, to route it solely to one or more HSR subsystem, to reroute an utterance originally sent to an ASR over to an HSR, to use HSRs to help tune and train one or more ASRs, and to use multiple ASRs to increase reliability of results.

In one aspect, the ASR proxy includes a recognition decision engine and a results decision engine. In a related aspect, these two engines facilitate recognition performance, natural language understanding and recognition and grammar tuning for accurately filling information slots in various business forms.

In a further aspect, the ASR proxy selects ASR and/or HSR resources based on one or more of application criteria, recognition confidence prediction, historical results, and recognition experienced with a particular user's voice.

In yet another aspect, the ASR proxy is configurable based on various parameters, such as maximizing use of ASR, or making communication more or less “human-like”.

In yet another aspect, the ASR proxy adjusts automatically to system resource capacity of HSR to maximize use of ASR or DTMF.

In yet another aspect, the ASR proxy uses the results of the evaluation component that analyzes ASR results to select one or more of: optimal lengths for length-based test, optimal quality metrics levels for user responses to different prompts and optimal classifier for different prompts.

In still another aspect, selection of ASR or HSR resources by the ASR proxy is transparent to a software application calling upon the ASR proxy for voice recognition.

In still another aspect, the system uses methods to predict successful automation recognition, in as close to real-time when using HSR, to maintain a more human-like experience.

Those skilled in the art will recognize that a particular configuration addressed in this disclosure can be implemented in a variety of other ways. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

The features described above may be used alone or in combination without departing from the scope of this disclosure. Other features, objects, and advantages of the systems and methods disclosed herein will be apparent from the following detailed description and figures.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features and various advantages will apparent from the following detailed description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which:

FIG. 1 is a block diagram illustrating one embodiment of an architecture of an interactive response system;

FIG. 2 is a flow chart illustrating an embodiment of a method for communication among a customer, the interactive response system and a human interface;

FIG. 3A is a chart illustrating one embodiment of a customer/interactive response system interaction in the context of FIG. 2;

FIG. 3B is a computer screen user interface illustrating one embodiment for capturing customer intent and data in the context of FIG. 2;

FIG. 4A is a chart illustrating one embodiment of a customer/interactive response system interaction in the context of FIG. 2;

FIG. 4B is a computer screen user interface illustrating one embodiment for capturing customer intent and data in the context of FIG. 2;

FIG. 5A is a chart illustrating one embodiment of a customer/interactive response system interaction in the context of FIG. 2;

FIG. 5B is a computer screen user interface illustrating one embodiment for capturing customer intent and data in the context of FIG. 2;

FIG. 6 is a flow chart of processing an email in the context of an interactive response system;

FIG. 7 is a block diagram illustrating one embodiment of an architecture of an interactive response system with a training subsystem.

FIG. 8 is an exemplary processing flow 800 for ASR training.

FIG. 9 is a high-level block diagram illustrating an example of a computer 200 for use as any of the computers/processors referenced herein.

FIG. 10 is a timeline representation of recognition of intents and data of an audio stream by different intent analysts.

FIG. 11 is a block diagram representation of an application interacting with an ASR proxy, showing the proxy's principal components.

FIG. 12 is a flow diagram illustrating process and decision flow of a recognition decision engine for determining whether to use ASRs, HSRs, or both.

FIG. 13 is a flow diagram illustrating process and decision flow of a results decision engine using a single ASR.

FIG. 14 is a flow diagram illustrating process and decision flow of a results decision engine using multiple ASRs.

FIG. 15 is a flow diagram illustrating process and decision flow of a results decision engine using both ASR and HSR.

FIG. 16 is a flow diagram illustrating process and decision flow of a results decision engine using HSR.

FIG. 17 is a time sequence illustrating the response gap with automated recognition and human-assisted recognition.

FIG. 18 is a block diagram representation of an application and interacting with an ASR proxy, showing the ASR proxy's principal components.

FIG. 19 is a flow diagram illustration the process and decision flow of a recognition decision and results decision with Statistics about the recognition and information about the System Status.

FIG. 20 is a flow diagram illustrating the recognition decision and results decision with ASR Statistics and System Status.

FIG. 21 is a flow diagram illustrating the recognition decision and results decision with timer ASR Statistics and System Status.

FIG. 22 is a flow diagram illustrating the recognition decision and results decision with predictor recognition ASR Statistics and System Status.

FIG. 23 is a flow illustrating a process to create the Statistics.

FIG. 24 illustrates examples of some recognition optimization criteria to create Statistics

DETAILED DESCRIPTION

Description of operation of an interactive response system, and of related machine learning systems and processes, is provided first, according to FIGS. 1-10. Description of operation of an ASR proxy system and processes related thereto are described thereafter, according to FIGS. 11-16. FIGS. 17-24 and the corresponding discussion generally relate to the process of optimizing the ASR proxy, with the goal of optimizing the combination of automation of computer recognition and human-assisted recognition, while at the same time improving the user experience. Note that unless otherwise evident, the terms “intent” and “meaning” used herein refer to the contextual reason corresponding to an utterance (for instance, having a system determine a caller's business intent to make a new flight reservation). In contrast, the term “recognize” and its derivatives is generally used herein for the process of converting a sound to its corresponding word.

A Human Assisted Decision Engine is used to implement a multi-channel and multi-modal system. It decides, based on a set of prediction data and capacity factor, after routing an “interaction” to automation and depending on the predictive results from automation, to use HSR prior to even the competition of the automated recognition. In some embodiments, the system automatically speeds up the “utterance” or “video” to further shorten the time gap between automation and human assistance.

The interpretation of responses to prompts can be viewed as two varieties of text analysis: information extraction and sense classification. Information extraction is the identification, extraction and normalization of specific fragments of information that are essential to filling the slots of business forms, such as customer IDs, telephone numbers, dates, addresses, product types, problems, and so forth. Sense classification has to do with the identification two additional types of information: meaning (intent) and response qualities. Meaning (intent) is concerned with what kind of form needs to be filled (such as, billing, scheduling an appointment, complaint, and so forth). Response quality is concerned with the response itself (such as, garbled, noise, in Spanish as opposed to English, desire to speak with a live agent and so forth).

This interpretation of responses can be done either by intent analysts alone (pure HSR), by automation (ASR and Intent Classification), or by some combination of ASR and HSR. Using confidence metrics in the results of ASR-automation to determine when ASR is generating reliable results, it is possible to trade off ASR automation for HSR with limited or no loss of quality. This means that the combination of these two approaches in the proxy processing system can achieve greater throughput than using HSR alone while handling peak demand loads with smaller teams of intent analysts.

FIG. 1 illustrates one embodiment of an architecture for connecting an interactions platform 102 to an interactive response system 100 through an interactive router 101 (herein referred to as an “iRouter”). As shown in FIG. 1, interactions platform 102 is connected to a customer 103 through communications link 104. Interactions platform 102 is also connected to interactive response system 100 at iRouter 101 via a datalink, which comprises a TCP/IP data link in this exemplary embodiment. Interactions platform 102 in this exemplary embodiment comprises a computer server. The exact configuration of the computer server varies with the implementation but typically consists of a Pentium-based server running an operating system such as Windows or Linux, using a voice board from a vendor such as Dialogic. Interactions platform 102 can also be an e-mail gateway or web server. Thus, customer input enters interactive response system 100 via telephone or intercom and text is entered via email or an interactive chatting interface (e.g., a web page or a stand-alone application such as Yahoo Messenger).

In this architecture of FIG. 1, in various embodiments a number of different types of devices are used to implement each of the interactions platform 102 and communications links 104. Interactions platform 102 may be implemented by any device capable of communicating with the customer 103. For example, interactions platform 102 is in one embodiment a telephony server in interactive response system 100 where the customer is calling by telephone. The telephony server handles answering, transferring and disconnecting incoming calls. The telephony server is also a storehouse for prerecorded audio clips so that it can play any welcome prompt and as other audio clips as directed by iRouter 101.

A telephony server in accordance with this embodiment is assembled from off-the-shelf components, for example Windows for an operating system, a central processor, such as a Pentium processor, and an Intel Dialogic voice board. Using this architecture, the communications link 104 is implemented by any means of providing an interface between the customer's telephone and the telephony server. For example, communications link 104 is in various embodiments a dial-up connection or a two-way wireless communication link.

In another exemplary embodiment, interactions platform 102 is a gateway server in interactive response system 100. In accordance with this exemplary embodiment, the customer interacts with the interactive response server by e-mail, interactive text chats or VOIP. The gateway server runs customized open source e-mail, www server software or SIP. Further, a gateway server in accordance with this exemplary embodiment is designed to conduct e-mail, interactive text chat, or VOIP transactions with customers, while also forwarding and receiving data to other elements of the system. Using this architecture, the communications link 104 is implemented by any means of providing an interface between the customer's computer and the gateway server. For example, communications link 104 is in various embodiments a dedicated interface, a single network, a combination of networks, a dial-up connection or a cable modem.

While only one interactions platform 102 is illustrated in FIG. 1, one skilled in the art will appreciate that multiple interactions platforms 102 may be used in this system after studying this specification. With multiple interactions platforms 102, an interactive response system may communicate via voice and text data with a customer. Further, multiple customer bases may be accommodated by a dedicated interactions platform 102 for each of the customer bases. In this manner, a workflow (as will be described further, below) is selected by determining which of the multiple interactions platforms 102 initiated the interaction.

In the architecture of FIG. 1, the iRouter 101 comprises software to control interactive response system 100. iRouter 101 “owns” the interaction with customer 103 from beginning to end by coordinating activity among other components and managing the transaction. iRouter 101 manages interactions with customer 103 according to one or more programmable scripts, called, according to this exemplary embodiment, “workflows.” In general, a workflow comprises an interaction flow wherein the path through the workflow depends upon intent input from the customer. Workflows are preprogrammed by system engineers and, advantageously, periodically “tweaked” in order to improve customer satisfaction, speed, accuracy, etc. In accordance with this exemplary embodiment, iRouter 101 is almost always “in charge” of selecting the next step or path in the workflow.

iRouter 101 receives interaction input from interactions platform 102 in the form of audio clips, email, text data or other interaction type—depending on the form of customer communication—and forwards the input to one or more human agents 105 (sometimes referred to as “Intent Analysts” or “IAs”), speech recognition engines or expert systems (collectively 108, and sometimes referred to as “automated speech recognizers” or “ASRs”) and uses the responses to advance its current workflow. When human interpretation (or translation) of the input is necessary, iRouter 101 directs human agent desktop software to display an appropriate visual context of the current workflow. Once iRouter 101 understands the input, iRouter 101 advances through the workflow and directs interactions platform 102 to respond appropriately to customer 103.

In an exemplary embodiment wherein interactions platform 102 comprises a telephony server, iRouter 101 delivers sound clips to play back to a customer, send text-to-speech clips or both. Alternatively, interactions platform 102 may store sound clips, have text-to-speech capability or both. In this embodiment, iRouter directs interactions platform 102 as to what to play to a customer and when.

iRouter 101 comprises, in this exemplary embodiment, a networked, off-the-shelf commercially available processor running an operating system such as Windows or Linux. Further, iRouter 101 software includes a modified open Voice XML (VXML) browser and VXML script incorporating objects appropriate to the specific application. One skilled in the art will understand how to construct these objects after studying this specification.

In accordance with the exemplary architecture of FIG. 1, interactive response system 100 includes at least one pool of human agents 105. A pool of human agents 105 is often located at a contact center site. Human agents 105, in accordance with the present embodiment of this invention, use specialized desktop software specific to system 100 (as will be described further, below, in connection with FIGS. 3B, 4B and 5B) that presents a collection of possible intents on their screen (their user interface)—along with a history or context of the customer interaction to that point. The human agent or agents 105 interpret the input and select an appropriate customer intent, data or both.

For telephone interactions, human agents 105 wear headphones and hear sound clips (“utterances”) streamed from the telephony server 102 at the direction of iRouter 101. In accordance with one aspect of this invention, a single human agent 105 will not handle the entire transaction for customer 103. Rather, human agent 105 handles some piece of the transaction that has been designated by the workflow designer as requiring human interpretation of customer's 103 utterance. IRouter 101 can send the same customer 103 interaction to any number of human agents 105, and may distribute pieces of a given interaction to many different human agents 105.

In accordance with the exemplary embodiment of this invention, human agents 105 are preferably off-site. Further, human agents 105 may be in diverse geographic areas of the world, such as India, the Philippines and Mexico. Human agents 105 may be in groups in a building or may be working from home. In applications that require 24/7 human agent support, human agents 105 may be disposed around the world so that each human agent 105 may work during suitable business hours.

Interactive response system 100 of the present invention employs custom human agent application software. Human agents 105 use a custom application developed in Java and running on a standard call center computer network workstation. Generally speaking, interactive response system 100 applies human intelligence towards interpretation of customer 103 input into “intent” (what the customer wants) and data (any input required to determine what the customer wants). The interpretation normally comprises selecting the most-correct interpretation of what was said from a list of choices, in this exemplary embodiment. In an alternate embodiment, computer-aided data entry (e.g., auto-completion of text entry or entry of an email address) is used in connection with agent processing.

Workflow server 106 of the present invention, an off-the-shelf component, is an archive of the workflows used by the Interactions router. Workflow server 106 is in one embodiment built with off-the-shelf hardware using a commercially available processor running a standard server operating system, with the workflow documents written in XML in this exemplary embodiment. Workflow server 106 maintains a compilation of business rules that govern the behavior of iRouter 101.

Interactive response system 100 employs a workflow designer used by a business analyst or process engineer to map out workflows. A workflow serves as the map that iRouter 100 follows in a given interaction, with speech recognition or human agents. The workflow “steers” iRouter 101 along a path in the workflow in response to customer input. A place in the workflow, along with data collected to that point is called a “context.”

The workflow designer builds instructions for human agent 105 into the workflow in order to guide human agent 105 in interpreting intent. The workflow designer may include a version of Eclipse software development environment customized to focus on building XML documents. However, one skilled in the art will be able to develop a workflow designer after studying this specification.

Performance and interactions archive 107 of the present invention comprises a database that can be maintained on any common computer server hardware. Performance and interactions archive 107 contains both archival data of system transactions with customers 103 (i.e., a repository of sound clips, e-mails, chats, etc. from interactions with customer 103) as well as performance data for human agents 105.

This exemplary embodiment employs “reporter” software to generate statistics about a group of interactions or to display performance ranking for human agent 105. Reporter software can also reconstruct an interaction with customer 103 from sound clips, e-mails, or chat text that constituted customer's 103 contact stored in interactions archive 107. Reporter software is a series of simple scripts, and can run on any common server hardware.

This exemplary embodiment also includes manager/administrator software, usually run from the same station as reporter software. Manager/administrator software sets operating parameters for interactive response system 100. Such operating parameters include, but are not limited to, business rules for load balancing, uploading changes in workflow, and other administrative changes. In one particular embodiment, manager/administrator software is a small custom Java application running on a standard call center computer workstation.

Support system 108 consists of numerous databases and customer proprietary systems (also including off-the-shelf automated speech recognition (ASR) software such as Nuance) that may be employed in responding to customer 103 requests. For example, support system 108 may include a database for customer information or a knowledge base. Speech recognition software is, in this exemplary embodiment, an off-the-shelf component used to interpret customer 103 utterances. Support system 108 may also include a text-to-speech capability, often off-the-shelf software that reads text to customer 103.

Company agents 109 of the present invention consist of human agents that handle customer 103 requests that the workflow refers to them. For example, should customer 103 intend to obtain assistance with a company matter, and an outsourced human agent 105 identifies that intent, the workflow may direct interactive response system 100 to transfer the call to company agent 109.

The elements of interactive response system 100 communicate over a TCP/IP network in this exemplary embodiment. Communication is driven by the workflow that iRouter 101 follows. “Database” in the present embodiment can be a flat file database, a relational database, an object database, or some combination thereof.

Turning now to FIGS. 2 through 5, these figures illustrate an example of how information is retrieved and handled by interactive response system 100 when a customer interacts with the interactive response system 100 via telephone. The example shown in FIG. 2 presupposes that all required hardware, software, networking and system integration is complete, and that a business analyst has mapped out the possible steps in a customer interaction using the graphic workflow designer. The business analyst also has scripted the text for anything that the interactive response system may say to a customer 103, including, but not limited to, the initial prompt (e.g., “Thank you for calling, how can I help you today?”), response(s) to a customer, requests for additional information, “stutter speech” (sounds sent to the customer while the iRouter 101 is determining a response), and a closing statement. Either text-to-speech software or voice talent records the server-side speech pieces as written by the business analyst. This workflow is then loaded into the interactive response system 101 where it is available to the iRouter 101.

As shown in block 201, the interaction begins with the customer 103 calling the customer service telephone number of a company. The interactions platform 102, in this case a telephony server, answers the telephone call and retrieves the appropriate workflow stored in the workflow database, based on either (1) ANI/DNIS information of the caller or (2) other business rules (e.g., line or trunk the call came in on), as illustrated at block 202. The telephony server then plays the appropriate welcome prompt as illustrated at block 203 and the customer then responds to that prompt (block 204).

For purpose of example, an imaginary airline, Interair, provides customer service via an interactive response system in accordance with a call center embodiment of this invention. The interaction platform 102 is therefore a telephony interface and iRouter 101 selects a workflow appropriate to Interair.

A first point or context in the workflow is shown in the illustrative workflow of FIG. 3A. There is no customer utterance, thus no intent or data to capture (and respond to). The only response is the greeting and the prompt for customer input.

Processing proceeds to box 204 in the flowchart of FIG. 2. The telephony server begins digitizing the customer's spoken input and connects to the iRouter. At this point, workflow or business rules determine if the interactive response to the customer needs to be handled by a human agent or speech recognition software. That is, the iRouter selects the appropriate workflow for the call from the workflow repository and follows the workflow rules to conduct a conversation with the customer.

To interpret customer speech, iRouter 101 uses ASR from the support systems or has the customer's audio streamed to human agents 105 in contact centers as appropriate, as illustrated in block 205. If human agents 105 are required by the workflow, iRouter 101 identifies available human agents by applying a load balancing algorithm, triggers a pop-up on their screens (as illustrated in the initially blank pop-up screen, FIG. 3B), presents several selectable intent options, and begins streaming customer audio to the identified human agents, as shown at block 207. This load balancing, at various times, includes identifying more or fewer human agents for interpreting the utterance based on any of a variety of factors, as will occur to those skilled in the art given the present disclosure. The human agent(s) hear the customer utterance in headphones, and computer software prompts for an interpretation of the utterance as shown in blocks 210 and 211.

In accordance with the exemplary workflow of FIG. 4A, the customer utterance that the human agent or agents hear is “I need to check my flight from Chicago to London this afternoon.” The agents' screen indicates the current context (or point in the workflow) as illustrated in FIG. 4B. In this illustrative screen shot, there are 12 possible requests (including unanswerable and terminate) that the human agent can select. In operation, there are several hundred possible interpretations available to the agents. Such multiplicity of selection allows the agents interpretive flexibility, which enables the iRouter to jump around in its workflow according to the interpreted intent. Thus, in accordance with one aspect of this invention, the iRouter can respond appropriately even if the customer changes subjects in midstream.

In each case, each agent selects what he or she feels is the best fit interpretation of the customer utterance in the current context of the workflow. In example of FIG. 4B, the human agent(s) selects “CFT” (Check Flight Time) and enters or selects from drop down menus the departure and arrival cities (or other, preprogrammed information that the customer could possibly utter).

Note that, in blocks 208 and 209, human agents can elect to apply acceleration to the customer audio clip(s) received at the station in order to compensate for any response delay (usually due to lag time in application set-up—the time it will take for human agent desktop software to accept the streaming audio and display the appropriate workflow). Network latency might be around 0.2 seconds, where application delay could be more in the 1+second range. To compensate for the application delay, the interactive response system accelerates the voice clip (although not to the point of discernible distortion). The purpose is to strive for a more “real-time” conversational interaction, so that the customer does not experience a notable delay while awaiting a response. The acceleration is applied to the speech as it is streaming from the telephony server. The acceleration can never overcome the inherent latency of the link but will allow human agents to “recover” any application set-up time and reduce the amount of lag time in the interaction, ideally up to the limits imposed by latency in the network. However, acceleration is optional, wherein a novice agent may need a slower playback, while a more experienced agent may apply acceleration.

In test 213, the iRouter evaluates the accuracy, in real time, of the customer audio interpretation and updates each agent's speed/accuracy profile. Next, in block 214, the iRouter processes the interpretation and performs the next step(s) in the workflow (e.g., database lookup based on input data) and then forwards an appropriate response 218 to the customer through the telephony server (if the interpretation is deemed accurate). If the iRouter determines the interpretation is accurate, it directs the playback of responses to the customer from the telephony server based on the interpretation of either the speech recognition software or by applying key algorithms to the responses of one or more human agents. In this example, the response is given in the last block of screen 2, FIG. 4A.

To determine accuracy, the iRouter compares the interpretation of two human agents, and, if no consensus is reached, plays the customer audio clip for a third human agent for a further interpretation (i.e., “majority rule” determines which is the accurate response). Other business rules may also be used to determine the accurate interpretation. For example, an interpretation from the agent with the best accuracy score may be selected. Alternatively, one of the interpretations may be selected and played back to the customer (“I understood you to say . . . ”) and the customer response determines whether the interpretation was correct. Further, the interpretations may be selected from known data (e.g., two interpretations of an email address could be compared against a database of customer email addresses, only one of two interpretations of a credit card number will pass a checksum algorithm, etc.).

The interactive response system allows for virtually any number of human agents to handle to same customer interaction at once. That is, an interactive response system could have two agents listening during a busy time or have seven human agents listening during a more idle time. Moreover, during times of high call volume, accuracy can be decreased by removing the “double-checking” rule to maintain high response time. An agent assigned a high trust ranking based on the agent's speed/accuracy profile may be asked to work without the double-checking. In addition to trading off accuracy for quicker system availability, a steady flow of audio clips is flowing by each agent, thereby decreasing human agent “slack” time.

Returning to the flowchart of FIG. 2, either the customer will respond again as seen in block 204, the call will be transferred (if so directed by a step in the workflow or by business rules), or the customer terminates the call, as shown in block 215. If the interpretation is deemed inaccurate in block 213, the iRouter 101 plays a stall speech to the customer (block 216) and sends the audio clip to additional human agents for another interpretation (block 217) and then reevaluate its accuracy.

The iRouter manages interaction with the customer to call completion, using the workflow as its guide. The iRouter may stream customer utterances to human agents for interpretation at numerous points in the call. Once the call has concluded, a snapshot of the customer interaction is preserved in the archive database. Human agents' speed/accuracy profiles are constantly updated and maintained.

If human intervention is not needed to interpret customer's request, ASR interprets the audio clip and the iRouter determines the appropriate response as shown in blocks 206 and 214.

Continuing with the Interair example, the captured customer utterance, as seen in FIG. 5A, has two requests: food and entertainment queries. In accordance with another aspect of this invention, the human agent captures two intents: meal and movie. There is no relevant data to enter because the interactive response system already knows the flight information from the previous data entered in FIG. 4B (this data is visible in FIG. 5B). As seen in FIG. 5B, the human agent enters “General” and “Meal” from an on-screen display of possible intents. The human agent also enters “Movie.” As seen in FIG. 5A, the interactive response system then provides the appropriate response. As seen in FIG. 5B, if the customer requests further information regarding the meal or movie such as: “what meal is offered?”, “Are their special meals?”, “What is the movie rated?”, the appropriate human agent interpretation options are located on the computer screen.

FIG. 6 illustrates an example of how information is retrieved and handled by the interactive response system when a customer interacts via electronic mail (email, as it is commonly known in the art). As shown in block 601, the interaction begins with the customer emailing to the customer service email address of a company. The interactions platform, in this exemplary embodiment, a gateway server, opens the email and retrieves the appropriate workflow stored in the workflow database based on either (1) the to/from information of the customer or (2) other business rules, as illustrated at 602. The gateway server then sends the appropriate response acknowledgement as illustrated at 602. Then the iRouter 101 identifies available human agent(s) to handle the email by applying a load balancing algorithm, triggers a pop-up on their screens to show possible intents for interpretation, and sends the email content to the or those human agents, as shown at block 603. The human agent(s) interpret the email as shown in blocks 604 and 605. After test 606, where the iRouter 101 evaluates the accuracy, in real time, of the customer email interpretation and updates each agent's speed/accuracy profile, the iRouter 101 processes the interpretation and performs the next steps in the workflow accordingly. Eventually, the iRouter 101 forwards an appropriate email response to the customer through the gateway server (if the interpretation is deemed accurate) as seen in block 607. The emails are then archived in the appropriate database as illustrated in block 608. If the interpretation is deemed inaccurate, the iRouter 101 sends the email to another human agent for another interpretation (block 609) and then reevaluates its accuracy. The iRouter 101 manages interaction with the customer to email response, using the workflow as its guide.

The discussion of an interactive response system and its constituent processes above in connection with FIGS. 1-6 includes operation of one or more speech recognition and related subsystems 108. In practice, implementation of IVR system 100 requires such subsystems 108 to be capable of recognizing a significant portion of the customers' utterances in order to minimize the need for human interaction.

Referring now to FIG. 7, a training subsystem 701 is included as a part of IVR System 100. In operation, training subsystem 701 selectively provides machine learning capabilities to real-time ASRs in subsystems 108 to allow them to very quickly adapt to new or changed customer interactions. For instance, when an IVR system 100 is first installed for a company, the generic capabilities of an embedded ASR may not be very usable for actual customer interactions, particularly if those interactions include many industry-specific terms (e.g., an electrician calling to order a ground fault circuit interrupter would typically use the acronym “GFCI”, which few ASRs would recognize easily). Likewise, when a new offering becomes available, existing ASR capabilities may begin to fail even though they were previously successful (e.g., an ASR that correctly identified “iPod” in past uses may begin to fail upon introduction of another product with a similar name, such as “iPad”). In some applications, these changes may be infrequent, while in others, they may occur on a regular basis. For example, an application for selling tickets to rock concerts will need to adapt to new customer requests for band names on a regular basis.

In one embodiment, training takes place based on the indicated need for such training. For an existing system in which ASR accuracy is well above a threshold of acceptability, training may occur only rarely, if at all. In such instances, training could occur, for example, only during periods of extremely low call volume during which IAs 105 are otherwise relatively idle. Where a system is new or whenever ASR success is dropping below acceptable limits, more training may be called for and so training subsystem 710 is active more often.

A non real-time training ASR 711 of training subsystem 710 receives as input a customer utterance from iRouter 101 and a corresponding intent from IA 105. In practice, multiple training ASRs 711 may be used as described below.

As with real-time production processing, processing for purposes of non real-time training includes in some embodiments inputs from single IAs and in others inputs from multiple IAs. Differences in intent selected by different IA's are quite helpful in training an ASR, as they may indicate a particularly nuanced utterance that calls for extensive additional training. In the simplest form, where a business intent may have a small grammar with very few options, such as “yes” or “no”, and where an ASR comes with a pre-packaged understanding of the utterances in “yes” and “no”, training may consist of building a statistical model that can be used for grammar tuning. In more complex training, the ASR is assisted in the recognition of words with domain knowledge, in order to build the statistical language model of the utterances that may be said.

In a preferred embodiment, IVR system 100 is implemented using multiple available real-time ASRs in support systems 108. In practice, each ASR is found to have strengths and weaknesses, and success in particular areas is usable by iRouter 101 to determine which ASR to use in a particular circumstance, as well as by training subsystem 710 to determine which ASR could benefit from training in a particular circumstance. Currently available ASRs include those from Carnegie Mellon University (Sphinx), Nuance, Dragon, Loquendo, Lumenvox, AT&T, SRI International, Nexidia, Microsoft and Google. As only select ASRs are available for no cost (e.g., under open source licenses), financial considerations may limit the number of ASRs to include in support systems 108. Because iRouter 101 can selectively route production requests to an ASR that is expected to perform well in any specific context, and because training subsystem 710 can likewise selectively train real-time ASRs based on expected improvement in their performance, it will often be advantageous to select a group of ASRs with performance characteristics that are somewhat orthogonal to one another. In that manner, one ASR can be expected to make up for weaknesses in another ASR. For example, an ASR optimized for processing telephonic speech may have performance characteristics quite different than one designed for speech from dictation equipment.

In order to increase accuracy of the real-time ASRs used with IVR system 100, training subsystem 710 facilitates machine learning by providing the real-time ASRs with training that is specific to the meaning of each received utterance, based on non real-time operation of training ASR 711.

Commonly ASRs are trained in several different aspects. First, ASRs must be able to classify audio streams, and portions of audio streams, into components that can help lead to recognition of a word that is being spoken. Typically, this involves identifying, within an audio stream, a set of similar sound classes known as “phones,” sound transitions or combinations known as “diphones,” and potentially more complex waveform portions referred to generally as “senones.” Commonly, utterances are divided wherever periods of silence are detected. Features are derived from utterances by dividing the utterance frames (such as 10-millisecond timeframes) and extracting various different characterizing aspects of the audio within that timeframe, such as whether amplitude and frequency are increasing, constant or decreasing. In the Sphinx ASR available from Carnegie Mellon University, 39 features are extracted to represent speech as a “feature vector.” Typically, ASR engines come with this aspect of their recognition fixed and users of such systems cannot change which features are analyzed or how they are analyzed.

ASRs use various models to proceed from raw audio waveform to a prediction of the word corresponding to the utterance. An acoustic model determines most probable features/feature vectors for received senones. A phonetic model maps phones and words, with the words coming either from a fixed dictionary or from a vocabulary (or “grammar”) derived by machine learning. A language model restricts candidate word choices based on some context, such as a previously recognized word. ASRs typically use a combination of these models to predict which words correspond to utterances. It is the latter two models, i.e., phonetic models and language models, that are the focus of training in the embodiments discussed below, although the concepts addressed herein could readily be applied to other models used in speech recognition.

In many instances, training an ASR can be more effectively accomplished by using context, either from previously recognized words or, for processing that is not in real time (i.e., later-recognized words in the same customer discourse). Such training is described below.

Turning first to phonetic models, consider the following user utterance: “I would like to fly roundtrip between Boston and San Diego.” An “off-the-shelf” ASR may have some difficulty recognizing some of these words across a variety of speakers. For example, in pronouncing the word “roundtrip” some speakers may elide the “d” and “t” consonant sounds into one sound (“rountrip”), while others may enunciate them separately (as if they were the two words “round” and “trip”).

In one embodiment, training subsystem 710 provides machine learning to non-real time training ASR 711 by addressing each of these issues. First, training subsystem 710 selects a target vocabulary based on a business meaning that corresponds to the utterance as determined by an IA 105 when the utterance was initially received. In this instance, the IA likely selected “New Reservation” as the business meaning. Whereas the word “roundtrip” may have been one word out of 40,000 in a general grammar, with a very low statistical rate of occurrence, it may be one word out of only 1,000 in a grammar specific to the “New Reservation” intent, and may have a statistical rate of occurrence that is far higher. Thus, training subsystem 710, by changing the applicable grammar, significantly increases the probability that training ASR 711 will accept the word “roundtrip” as what was spoken, even if the feature vectors vary significantly from a standardized model of that word. Furthermore, as additional utterances of “roundtrip” become associated with the “New Reservation” intent, those utterances likely will more closely match at least some of the previously recognized instances in which “roundtrip” was spoken. Thus, over time both the likelihood of the word “roundtrip” occurring in a “New Reservation” intent and the variations in pronunciation of that word will lead to two results: (a) greater certainty in recognizing the word (which can be propagated to other grammars that include the same word, such as a grammar associated with the “Cancel Reservation” intent); and (b) better ability to predict business intent by refined statistics as to how often the word is associated with a particular intent.

Returning to the utterance example used above, fast-talking speakers may blur the distinction between “Boston” and the following word “and,” and may fail to articulate all of the sounds such that training ASR 711 may be trying to analyze a sound, “Bostonan.” Likewise, the city name “San Diego” may be pronounced by some speakers in a manner that sounds more like “Sandy A-go.” The selection of a “New Reservation”-specific grammar rather than a generalized grammar would again likely dramatically increase the statistical likelihood that recognition of “Boston” and “San Diego” will be achieved with confidence. As a further refinement, training subsystem 710 employs iterative passes through the utterances of an entire user discourse to improve training even further. In the example given above, it may be that later in the discourse the caller says “Boston” at the end of a sentence, in a manner readily recognized by training ASR 711. That speaker's acoustical signature for “Boston” is included in the ASR's mapping, so that on a second pass, the same speaker's “Bostonan” utterance will be considered a better match for “Boston” than it was before. Similarly, the speaker may say “San Diego” a second time in a manner that provides more distinction between “San” and “Diego,” thereby providing learning that upon an iterative recognition attempt will lead to a greater likelihood of successful recognition of the first, blurred utterance. For extensive customer discourses, multiple iterations may lead to significant improvement in overall recognition, as the caller's voice characteristics become better understood through the words that the system can recognize.

Referring now also to FIG. 10, in one embodiment the actual time of recognition by an intent analyst is used to decompose an audio stream into separate utterances for recognition (e.g., by training ASR 711). Specifically, the time of recognition of the utterance intent “I want to take a flight from” (1001, 1004), the time of recognition of the data portion “Boston” (1002, 1005), and the time of recognition of the data portion “San Diego” (1003, 1006) are all sufficiently distinct that the timeframes themselves are usable to facilitate decomposition of the audio into separate utterances for recognition. In some instances, an IA may provide recognition before (or after) the utterance is complete (e.g., as shown in FIG. 10 at 1003, “San Diego” is recognized by the IA before the final “o” sound), so in such cases time frames are adjusted to end at suitable pauses after (or before) the IA-provided recognition. The number of possible business intents and typical words used to express them are usable to narrow the intent recognition grammar, and the type of data collected (e.g., city names) are usable to narrow the data recognition grammar.

Moving on to language models, training system 710 again takes advantage of business intent to assist with training. For instance, where an IA has indicated a business intent of “New Reservation” it may be statistically quite likely that at least one instance of the word “and” in the utterance will be preceded by one city name and followed by another city name. Likewise, if the words “from” or “to” are recognized, it may be statistically very probable that a city name follow those words. In contrast, if a business intent determined by an IA is “seat assignment,” those same words “from” and “to” may rarely correlate with an adjacent city name but a nearby number-letter pair instead (e.g., “I would like to change from seat 39B to seat 11A.”).

Such language model training also allows for ready adaptation to changing user phrasings. For example, if an airline begins service to England, it may suddenly start receiving requests using different language than was used before, for the same business meaning. For instance, the prior example of “I would like to fly roundtrip between Boston and San Diego” might be spoken by a British customer as “I would like to book a return trip between Boston and London.” Initially, the word “book” and would not appear with high probability in the “New Reservation” grammar, but statistical usage of that word in that grammar quickly increases with additional British customers. Likewise, use of the term “return” changes with the addition of a British customer base, and the “New Reservation” grammar is adjusted accordingly to recognize this.

Training subsystem 710 also adjusts statistics for recognition candidates based on a combination of business intent and adjacent recognized words in the discourse. Consider the example in which a business intent has been determined as “New Reservation” and only one utterance in a user's discourse is not initially recognizable with a usable level of confidence. If the discourse is recognized to have included only one city name, the probability that the unrecognized utterance is another city name is quite high; the probability that it is a city name served by the airline using the system is higher yet. Changing the probabilities for candidate words within a grammar to recognize the partial recognition may well drop some candidate words from further consideration and may bring only one candidate (presumably a city name) to a usable level of certainty. Machine learning then incorporates that particular user's enunciation of the city into the ASR's model so that subsequent instances of similar utterances are more readily recognized.

Maintenance of separate grammars for each allowable business intent facilitates training subsystem 710 to provide more rapid teaching of ASRs than would otherwise be possible. For example, there are strong phonetic similarities in the utterances “book,” “notebook” and “Bucharest.” Determining which of these meanings corresponds to a user's utterance is greatly enhanced by considering the business intent. For example, if the business intent is “Lost & Found,” then “book” (in its noun sense) and notebook (as in “notebook computer”) may appear with much higher likelihood than in other contexts. If the business intent is “New Reservation,” then “book” (in its sense as a verb) may also appear with fairly high likelihood. Similarly, if the business intent is “New Reservation,” then “Bucharest” may appear with higher likelihood than if the business intent were, for instance, “Seat Selection.”

Once training ASR 711 has itself been sufficiently trained, correlations between business intents and language models can be developed in a very robust manner. For instance, one exemplary portion of a mapping for similar-sounding words might be as follows:

Business Intent Words and Probability New Reservation Book (0.8) Bucharest (0.1) Brook (0.1) Lost & Found Book (0.7) Notebook (0.2) Bucharest (0.1) Seat Selection Bulkhead (0.8) Bucharest (0.1) Book (0.1)

Training ASR 711 is particularly well-suited to develop language model statistics because it has two advantages over real-time ASRs from support systems 108. First, since it is not used for production operations, it does not need to operate in real time and can therefore take advantage of more complex recognition algorithms that could not, at least on relatively modest computing platforms, perform recognition quickly enough to be used for real time processing. This allows training ASR 711 to recognize utterances that real time ASRs in support systems 108 would not be able to recognize. Second, training ASR 711 can take advantage not only of a priori information from a customer discourse, but a posteriori information as well. Thus, it can wait until all utterances in an interaction are analyzed and then take multiple passes at recognition, presumably with greater likelihood of success on subsequent iterations. As noted above, an initial user utterance that sounds like “Bostonan” may be far more readily recognized after a second utterance of “Boston.”

Training ASR 711 builds, over time, a set of statistics related to language elements used with each associated business meaning. In one embodiment, multiple training ASRs 711 are used, each one contributing to the overall statistics. In some embodiments, the statistics include measures of certainty regarding recognition, based on multiple instances of recognition by a single training ASR 711, on agreement between multiple training ASRs 711, or both.

The statistics developed in this manner are usable by any of the real-time ASRs in support systems 108. Each of the different ASRs that may be used for real-time recognition in support systems typically has its own mechanism for training and corresponding specifications for how language models may be input to it for training. In a preferred embodiment, training subsystem 710 formats the statistics it develops for each of the ASRs in support systems 108 so that each of those ASRs can take advantage of the statistics generated by training subsystem 711. In practice, ASRs vary widely in the mechanisms they support for training, and training algorithm 712 is therefore readily configurable to collect, format and provide to ASRs training data in a manner appropriate for each existing ASR as well as new ASRs that may be added to support systems 108. As the performance of a real-time ASR improves with training, the quality of its recognition may allow it to replace the function of an IA 105 in processing 210,211.

Training subsystem 710 also works in conjunction with the capabilities of each ASR to ensure that the ASR training is leveraged maximally for use in IVR system 100. For example, an ASR may support a threshold determination for when sufficient portions of an utterance are recognized to be usable to perform statistical analysis, such as using sentence trees, and training algorithm 712 is configured to be compatible with such features so as to determine training progress.

The real-time ASRs in support systems 108 are used in two different ways calling for different statistical processing. In a first manner, they are used to recognize a process once an IA has determined a corresponding business intent. For example, one or more IAs 105 may select “New Reservation” as a business intent for a sentence spoken by a caller, and based on that one or more real-time ASRs in support systems 108 will attempt to recognize the specific words spoken by the caller.

In a second manner, a real-time ASR rather than an IA is used to determine the business intent. This is a different recognition task than determining the specific words spoken by the caller. For instance, determining whether a business intent may be “New Reservation” or “Seat Request” may involve recognition of a small number of highly likely key words specific to each intent, such as the words “from” and “to” for “New Reservation,” and the words “aisle” and “window” for “Seat Request.” One type of ASR in support systems 108 may be better suited to determine business intent, and another may be better suited to recognize words based on a that business intent. In one embodiment, the format of training statistics for each real-time ASR provided by training subsystem 710 is adjusted based on whether the real-time ASR is to be optimized for determining intent or recognizing words based on a determined intent.

Part of the training process includes determining how effective machine learning has been for the real-time ASRs in support systems 108. This is referred to as validation. In a preferred embodiment, validation is performed by training subsystem 710; in alternate embodiments validation is performed by iRouter 101 or a dedicated validation processor (not shown). In validation, ASRs are operated in parallel with one another and with IAs to determine how their performance compares. Each training instance provides more information that is used to develop statistical models and probabilities of grammar uses for each business meaning provided by the IAs. In some situations, historical data from IAs also determine the anticipated level of automation that may be available with respect to an utterance. If IAs routinely provide multiple meanings for an utterance, it may be that an ASR will only be usable if it is capable of significant contextual training. Those ASRs that have robust context processing may be able to properly process such utterances while those that are not contextually strong may be incapable of meeting a minimum threshold regardless of how much training is provided. As an example, the utterance “IP” could mean “Internet Protocol” or “Intellectual Property.” If used in an application where both meanings are common, mistakes in processing accuracy are to be expected unless an ASR is capable, after training, of deriving which of the two meanings is the appropriate one.

As training proceeds, the performance of a real-time ASR improves. At a point of statistical stabilization that meets the needs of the particular use of the ASR within IVR system 100, the ASR is placed into production operation. For example, an ASR intended to determine a business meaning for an utterance may operate in a non-production mode in parallel with an IA until such point as it has been trained enough that its performance approaches that of the IA, at which time it is switched to production operation to relieve the load on the IAs in processing 210, 211.

In a typical embodiment, in both real time production processing and training processing, input from two IAs is provided to two ASRs to increase accuracy. Should input from two IAs for the same utterance in the same user discourse differ, in some embodiments the utterance is submitted to a third IA (in some instances selected based on a measure of IA quality) for determination of meaning.

When an ASR reaches a level of accuracy above a certain threshold, as determined through validation and based on the specifics of the environment, training processing transitions. In one exemplary environment, the ASR is used for production processing but training continues as described above. In a less demanding environment, or in one with fewer available resources, training ceases altogether. In a third environment, training continues but at a reduced priority (e.g., training processing occurs only when there is a certain amount of available processing capacity or when the performance of the ASR is found to have deteriorated to a certain degree).

In some embodiments, a validation processor is configured to test ASRs to determine their performance levels. In some embodiments, validation follows a training phase, while in others it is performed concurrently with training. Based on results from validation, iRouter 101 changes its allocation of utterances to ASRs and IAs. For instance, if an ASR is found to perform sufficiently well in comparison with an IA in determining a business meaning, iRouter 101 routes utterances to that ASR far more often than to the IA. Advantageously, such routing is highly adaptable and configurable. Following the example used in connection with FIGS. 3-5, based on performance statistics, iRouter 101 may favor an IA for response interpretation immediately after a welcome message (FIG. 4B), favor a first ASR for response interpretation for movies or meals (FIG. 5A), and favor a second ASR for response interpretation for seat assignments, plane info, and select other choices shown in FIG. 5B. In some embodiments, two ASRs (as in 210, 211) are selected for each specific area of interpretation to ensure accuracy. If both provide the same interpretation, the corresponding response is provided to the user. If the ASRs differ, then the utterance is provided to an IA to select a meaning through adjudication as in 217.

As a result, human IAs are required only at specific times when ASRs fail to adequately perform, and processing may flow back to the ASRs immediately after IA intervention depending on business criteria, with no need for an IA to stay connected to the customer discourse. Where training can improve ASRs, it does so without imposing significant additional cost or other overhead on the overall IVR system 100. Human interaction does not need to be any more involved than listening to a single user utterance and selecting a meaning, or intent, of the user from a drop-down list of predetermined options so that an appropriate automated response is provided to the user.

Referring now to FIG. 8, an exemplary processing flow 800 for ASR training is illustrated. A digitized audio stream with a user utterance is provided 801 to one or more IAs 105 and, if the IAs are able to provide a usable intent response as described in connection with FIG. 7, to training ASRs 711. If a training ASR 711 cannot sufficiently recognize 802 the utterance so as to convert the audio to a textual counterpart, the utterance is discarded and not used for training.

If the ASR 711 can sufficiently recognize 802 the utterance, statistical models/tuning grammars (e.g., grammars corresponding with IA-provided meanings and data) are built 803 as described above in connection with FIG. 7. For some of those utterances below a certain confidence threshold determined by the ASR 711, an additional verification loop for an IA to verify the recognition by the ASR 711 of the intent or data can be utilized. If the recognition is verified, processing proceeds as described for 803, but if not, the recognition results are discarded.

Next, a test is made to determine 804 whether performance of the training ASR 711 is now sufficient. The performance threshold may depend on the criticality of the application. A health care application may be much less tolerant of errors, for instance, than a free tourist information service might be. The performance threshold may also depend on the rate that new words or phrases are added to the statistical model. If the performance is not sufficient, processing returns to prepare for further utterances that can be digitized 801 and used for additional training. If performance is sufficient, the results of training are applied to configure 805 the real time ASRs of support systems 108 with the models that have resulted from the training, and those real time ASRs are then validated and, if appropriate, used for production processing.

In some embodiments, training is then considered complete. The ASR is brought on-line initially in a provisional mode, i.e., as a shadow to the IAs. If the ASR meets a level of quality as determined by business criteria (e.g., by comparing results from the ASR and one or more of the IAs), it is brought into full production use so that it replaces an IA in processing 210. Likewise, performance of a second ASR is measured and, if it produces sufficient quality in recognition, it is brought on line to replace a second IA in processing 211. In other embodiments, further testing 805 is done, at times dependent upon the specific environment, to see whether performance of an ASR has dropped below some applicable minimum threshold. If it has, flow returns to 801 for additional training. If performance is acceptable, processing loops back to 805 to repeat testing at an appropriate time. Should performance not reach an acceptable threshold after numerous attempts, in some embodiments training is abandoned.

FIG. 9 is a high-level block diagram illustrating an example of a computer 200 for use as any of the computers/processors referenced herein. Illustrated are at least one processor 902 coupled to a chipset 904. The chipset 904 includes a memory controller hub 920 and an input/output (I/O) controller hub 922. A memory 906 and a graphics adapter 912 are coupled to the memory controller hub 920, and a display device 918 is coupled to the graphics adapter 912. A storage device 908, keyboard 910, pointing device 914, and network adapter 916 are coupled to the I/O controller hub 922. Other embodiments of the computer 900 have different architectures. For example, the memory 906 is directly coupled to the processor 902 in some embodiments. In some embodiments, components such as keyboard 910, graphics adapter 912, pointing device 914 and display device 918 are not used for certain of computers 200 not requiring direct human interaction (e.g., certain server computers).

The storage device 908 is a computer-readable storage medium such as a hard drive, compact disc read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 906 holds instructions and data used by the processor 902. The pointing device 914 is a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard 910 to input data into the computer system 900. The graphics adapter 912 displays images and other information on the display device 918. The network adapter 916 couples the computer system 900 to the Internet 1001. Some embodiments of the computer 900 have different and/or other components than those shown in FIG. 9.

The computer 900 is adapted to execute computer program modules for providing functionality described herein. As used herein, the term “module” refers to computer program instructions and other logic used to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules formed of executable computer program instructions are stored on the storage device 908, loaded into the memory 906, and executed by the processor 902.

The types of computers 900 used by the components described herein vary depending upon the embodiment and the processing power used by the entity. For example, a customer's computer 103 typically has limited processing power. The iRouter 101, in contrast, may comprise multiple servers working together to provide the functionality described herein. In some applications a single processor (or sets of processors) may implement both real time ASRs in support systems 108 as well as training ASR 711 and other functions of training subsystem 710. In those applications, determining how much training to do and when allows relatively inexpensive and modestly powerful computers to be used for both training and production ASR processing.

The systems and methods described above are applicable not only to voice interactions, but in certain embodiments is usable with, e.g., video, text, email, chat, photographs, and other images. These other embodiments are usable in applications such as on-line chat, security surveillance, theme park concierge services, and device help, for example. As a specific example, a consumer device such as the iPhone or iPad devices offered by Apple, Inc. can be provided with help facilities in which open-ended questions are interpreted and processed in the manner set forth above. Likewise, recognition of video streams and images can also be facilitated using the techniques as described above.

As is evident from the discussion above, ASR subsystems are sometimes more appropriate for handling a portion of a customer interaction than are HSR subsystems. In order to provide the best possible user experience, where an application program (such as stored in workflow repository 106) seeks speech recognition resources, benefits can be achieved by optimizing selection of the resources used for such recognition (i.e., ASR or HSR, as well as selection of particular ASR/HSR resources that are best suited for the current recognition task).

Referring now to FIG. 11, there is shown a block diagram of operation of an ASR Proxy 1102 to accomplish such selection of appropriate processing resources. More specifically, the functions described below are implemented in various embodiments by one or more of encapsulation in Media Resource Control Protocol (MRCP) within a Voice eXtensible Markup Language (VXML) browser, a web service, and an Application Programming Interface (API, for instance written in the Java or C# languages). In a specific embodiment, common ASRs from various vendors use MRCP as a standard interface to VXML platforms (browsers), and in this environment the ASR proxy 1102 is configured to appear to a software application 1101 running with the VXML platform as an ASR engine, but instead acts as a proxy between a VXML application and speech recognition functions by providing speech recognition resources from both ASR and HSR subsystems.

As set forth in greater detail below, ASR proxy 1102 is configured to freely select one or more ASR subsystems 1104 (such as described earlier in connection with discussion of support systems 108) or HSR subsystems 1106 (such as described earlier in connection with discussion of off-site agents 105). Based on a database subsystem of statistics 1105, ASR proxy 1102 communicates with a recognition decision engine 1103 (the operation of which is further described in connection with FIG. 12) and a results decision engine 1107 (the operation of which is further described in connection with FIGS. 13-16) to make determinations about which ASR/HSR resources 1104, 1106 to employ at any particular time. If any HSR resources are selected for use, corresponding user interface information is provided to an appropriate HSR desktop workstation 1108 as described above in connection with off-site agents 105.

ASR proxy 1102 alleviates the need for the developer of software application 1101 to consider whether an utterance is to be recognized by an ASR or an HSR. Accordingly, such a software developer can construct (and assume availability of) more human like voice user interfaces than have traditionally been used with computers.

With more specific reference now to FIG. 11, in various embodiments software application 1101 served various purposes. In one embodiment, software application 1101 is an IVR system for toll-free caller assistance; in another it is an interactive help application on a tablet computer. Software application 1101 directs ASR proxy 1102 by telling it what to recognize (i.e., providing it a grammar) as well as providing it with an utterance, typically an audio file, such as a .wav file or a real-time audio stream (e.g., an MRCP Real-Time Protocol stream). ASR proxy 1102 responds, as expected, with the “text” or meaning of what it recognizes, along with a confidence score that indicates the ASR's confidence that it has correctly recognized the utterance.

Because ASR proxy 1102 may have capabilities that differ from a conventional ASR, ASR proxy 1102 may require additional information in for example grammar meta-tags for statistics and decisions, such as a unique way of identifying the prompt and grammar, a unique way of identifying the current session, a unique way of identifying the “voice” or user (to continue to learn the acoustic model of the speaker), and thresholds to specify the behavior of the ASR proxy 1102. In some applications, grammars are predefined or built-in; in others, grammars are not built-in such that meta-information relating to grammar, such as user interface information to frame and guide the decision of the agent, is provided to better define possible responses (e.g., for an HSR subsystem).

When software application 1101 requests ASR proxy 1102 to recognize an utterance, ASR proxy 1102 passes processing over to recognition decision engine 1103, which is responsible for deciding how to recognize the utterance. For instance, parameters and confidence thresholds provided by software application 1101 may impact this determination. As a specific example, if an application calls for extremely high recognition quality, recognition decision engine 1103 may direct that recognition be accomplished solely by HSR resources 1106. On the other hand, the application may consider cost to be of paramount importance, and as a result may dictate that ASR resources 1104 be used exclusively as a default, reserving HSR resource 1106 usage only for instances in which ASR usage is resulting in numerous errors.

In one embodiment, ASR proxy 1103 automatically and dynamically makes similar decisions, varying appropriate thresholds to satisfy particular requirements of the application. Thus, a high quality threshold may be used for a high asset bank customer, while a utility bill inquiry from a consumer is given a lower acceptable threshold. In this embodiment, thresholds are based on historical statistics calculated based on past recognition attempts.

It is found that beneficial results are obtained by not merely selecting between use of ASR and HSR resources, but also by allowing selection of combinations of such resources. For example, one set of parameters may best be met by submitting an utterance for recognition by multiple ASR resources; another may best be met by submitting to a single particular ASR; and still another may best be met by submitting to a mix of ASR and HSR resources. In practice, issues such as the extent to which ASRs have been trained or tuned (per the training discussion above, for instance), whether an ASR has been validated for a particular grammar, whether a cost for multiple recognition paths is acceptable, and historical results are all helpful in determining which resources to apply in any specific situation.

Similarly, a security meta-tag relating to an utterance is helpful to determine the most appropriate recognition resource; for instance, a meta-tag indicating an utterance is a Social Security Number might be sent for processing by an ASR resource to avoid the potential for a human to obtain personal information about an individual.

Another parameter considered in certain embodiments is the level of activity of various system resources. If human staff is backlogged by a high volume of requests, that backlog is usable as a parameter to favor increased use of ASR resources.

Multiple resources, whether of the same or differing types, are in some embodiments used to provide dual-checking of results.

In yet another embodiment, recognition decision engine 1103 dynamically keeps track of the length of a current audio stream and compares it with lengths of expected utterances as defined by the corresponding grammar. For instance, if an utterance is expected to have a grammar consisting only of one of the three colors, “red”, “green” and “blue” and the actual utterance is three seconds long, a previous decision to have the utterance recognized by an ASR resource may be changed to an HSR resource in addition to or instead of the ASR, based on an anticipation that the utterance will NOT be one of the expected single syllable colors in the grammar. Such an approach is found to minimize the ultimate time to recognize “surprising” utterances and therefore increases the overall efficiency of the ASR proxy 1102.

As previously mentioned, operation of ASR proxy 1102 and the corresponding engines 1103, 1107 make extensive use of statistics, thresholds, and other unique information for personalization of a system to correspond to the needs of software application 1101. This information is stored in a statistics database 1105, as illustrated in FIG. 11. For example, the results of operation of an ASR are stored as a confidence score statistic in database 1105, and the aggregate statistics for that ASR is considered with respect to whether it is usable under the applicable business or other rules needed by software application 1101. Furthermore, any and all statistics about the utterance, such as the speaker, the prompt, the grammar, the application, the recognition methods (e.g., ARS, HSR, single ASR, multiple ASR, multiple HSR), the confidence, no-match or no-input, and training/tuning, are stored by ASR proxy 1102 in database 1105.

In a manner similar to what was described in connection with prior figures, if an ASR fails to provide usable results for an utterance, it is sent to HSR resources for recognition/resolution of disparities. Statistics are maintained not only for ASRs, but for HSRs as well, and statistics are further maintained on the basis of an individual speaker. Thus if an ASR is found to be particularly effective at recognition for a particular speaker, the statistics are maintained and updated so as to increase the likely use of that ASR for later utterances from the same speaker. Likewise, statistics are maintained on an individual grammar basis, again to maximize the likelihood that recognition decision engine chooses an appropriate resource to use based on the expected grammar or prompt/grammar combination. For example, a “yes/no” grammar may be more effective for a simple prompt recognition by an ASR such as “Are you John Smith?”, but less effective for a more complex question, such as “For today, relative to the same day last week, are you feeling better?”.

Generalizing from the above, statistics are generated on various bases, and are maintained so that intelligent decisions are made regarding when to use a particular ASR/HSR resource. Based on confidence levels, grammars capable of high confidence ASR recognition can even be used more often by software application 1101. For example, a “yes” or “no” grammar may have very high confidence with a simple ASR resource. Statistics are recorded about the prompt/grammar combination from simple confirmation statements such as “I have your phone number as (555) 123-4567. Is that correct?” to more complex communications such as, “If you have been feeling OK over the past week say ‘yes’ and if you have been feeling bad at all say ‘no’.”

Discussions herein regarding grammar are expandable and generalizable to combinations of grammar with prompts. One statistic, for example, relates to overall confidence for a set of utterances of the current speaker in the current session (i.e., over multiple prompts). If ASR recognition is failing for a speaker regardless of the prompt/grammar combination, that indicates ASR proxy 1102 would do better to resort to HSR for this speaker than even try ASR. On the other hand, if a particular speaker's utterances are routinely showing strong confidence, the ASR proxy uses ASR as the preferred recognition method. In order to generalize beyond a particular session, a unique speaker reference ID allows the system to recognize a particular speaker (e.g., based on phone number used to connect with the system) so as to choose an appropriate ASR or HSR resource.

Software application 1101 provides thresholds as the software developer may find appropriate for a particular situation, and in some situations are generated over time based on prior recognition experiences. For example, where statistics can be generated via dual checking or confirmation via an HSR resource, those statistics are collected and stored in database 1105. The mean, standard deviation and mode information from such statistics are applied to a range of thresholds depending on the needs determined by the software developer of software application 1101, based on the overall goals of such application.

Furthermore, statistics are usable to determine when further reliance on an ASR resource would not be effective. For example, if a significant sample size of recognition quality for an ASR and a specific grammar shows that performance is unlikely to rise over an acceptable recognition threshold, that ASR is removed from future consideration for that particular recognition task. This recognition task could require more training (or tuning), though through multiple training/tuning attempts, which prove unsuccessful, that particular recognition attempt is permanently removed from consideration until changes occur, such as an adjustment to the prompt/grammar or the use of a new ASR or a new version of an ASR.

Statistics are also usable for tuning ASRs. Tuning grammars are sometimes purely statistical, such as the percent of time “red” is used in the grammar “red, green or blue”, or can include synonyms such as “turquoise” for “blue.” In the latter case, tuning is facilitated through use of HSR resources for “out-of-grammar” recognizers (e.g., to confirm that in a particular instance “turquoise” should be considered synonymous with “blue”). Immediately after such tuning, it may be desirable in certain applications to introduce the tuned ASR in a “silent” limited test basis rather than a product basis to ensure performance is above acceptable thresholds. In one embodiment, HSRs are employed to verify that an ASR is capable of recognizing a grammar of interest; to calculate confidence threshold statistics during the validation phase referenced above; and to calculate confidence threshold statistics in the case of invalid recognition by the ASR. Even after validation, random dual checking with ASR or HSR resources provides ongoing checking of validity of a selected recognition method. The frequency of such checking is in one embodiment based on statistical deviations between correct and incorrect ASR recognitions. As a specific example, consider a situation in which an average confidence of a correct recognition is 56 and an average confidence of an incorrect recognition is 36. If the standard deviations are small (e.g., 8), this would suggest that there is little practical confusion between correct and incorrect recognitions, so dual checking does not need to be used very frequently. However, if the standard deviations are larger (e.g., 12), more frequent dual checking may be required to more finely tune the grammar confidence thresholds.

Over time, statistics may suggest that ASR proxy 1102 change its initial operations. For example, statistical suggestion of very good success may suggest changing from dual checking of two ASRs to only one; or with poor success from ceasing attempts to train or tune for a particularly difficult grammar, using HSR only instead.

Both initial training, and subsequent tuning, of ASRs share common characteristics and can be implemented similarly. However, in many instances training involves more subtle issues than initial tuning, larger vocabularies and statistical language models, so techniques that work well in tuning may not be optimal for training. Training may call for significantly larger sample sizes, greater use of HSRs, and reliance on out-of-grammar ASR resources.

Particularly complex grammars may call for consistent dual-checking by two ASRs with different recognition models (from different vendors), with differing outcomes being adjudicated by an HSR. Reliance on multiple HSRs (for instance, two with a third acting to resolve differences) may in some instances provide further benefits. See, e.g., U.S. Pat. No. 7,606,718, the contents of which are incorporated by reference as if fully set forth herein. ASR proxy 1102 is configurable, via software application 1101, to address any of these possibilities.

Turning now to FIG. 12, in one embodiment a recognition decision engine 1201 operates as follows to decides how to process an utterance depending on historical statistics (e.g., for a speaker, session, grammar and application) and other factors, as well as based on various configuration settings. In the example illustrated in FIG. 12, as an initial step software application 1201 may direct that an ASR not be used until it is trained or tuned. A check 1202 is made to determine that. If so, a check 1207 is made to determine whether such tuning/training has already been completed. Otherwise, a quick check 1203 is made to determine whether the grammar is simple enough so that training is not required (for example, the grammar has only a very small number of terminals). If the grammar is not simple, processing again flows to check 1207. If the grammar is sufficiently simple, processing flows to check 1204. Check 1207, referenced above, reviews stored statistics on the ASR success for the grammar, and for whether the ASR was previously tuned/trained for the grammar (whether in the same application 1101 or others that may have similar goals and corresponding thresholds of confidence). If those statistics indicate sufficient training/tuning, check 1207 passes processing on to check 1204. Otherwise, processing is directed to HSR processing 1210.

Check 1204 uses confidence statistics stored in database 1105 and a threshold that the ASR is capable of understanding a specific grammar, and a second statistic in the ongoing confidence of recognizing a speaker within a session. For those simple grammars that are not tuned or trained, on-going statistics of how well the ASR is performing the recognition task are compared with an anticipated recognition confidence threshold provided by the application or through a calculated threshold by the proxy. For instances in which a first recognition is being performed, the threshold can be set so that it is automatically considered not met, forcing recognition by an HSR to allow initial calculation of threshold by the proxy; in some embodiments the threshold is augmented by historical information regarding the current grammar. Additionally, if the ability of the ASR to recognize the speaker suggests a confidence above a threshold, ASR processing will be used and processing flows to check 1205. Otherwise, HSR processing 1210 is used. For example, a threshold may be set as the number of times the ASR recognition falls below the confidence (or adjusted confidence, e.g., high value speaker). In some applications, this is set as low as one ASR recognition below confidence to force subsequent recognitions to be performed by HSR.

Check 1205 determines whether software application 1101 or another component (e.g. requirements for training or validation) requires that a dual check be used for recognition. If such is not required, processing flows to step 1206 where a single ASR is used for recognition.

If a dual check is needed, processing flows to check 1208 to determining whether the dual check can be done by two or more ASRs (for instance, because there are two or more trained and otherwise acceptable ASRs available). If so, processing flows to step 1209 in which recognition is performed by such multiple ASRs. If not, for example where the ASR is not suitable for the recognition or to perform ASR validation, processing flows to steps 1210 and 1211 so that recognition is performed by both ASR and HSR resources.

When an ASR or HSR completes recognition, statistics regarding the recognition are stored in statistics database 1105.

As noted above in connection with FIG. 11, ASR proxy 1102 also communicates with a results decision engine 1107. The purpose of such an engine is to evaluate results of a recognition process by ASR/HSR resources. Referring now to FIG. 13, there is shown an exemplary results decision engine 1301, the operation of which is described as follows. Results decision engine 1301 reviews the results of recognition from one or more ASR/HSR resources and determines appropriate next steps. First, a check 1302 is made to determine whether a reported confidence levels meet a recognition threshold set by software application 1101 or calculated by ASR proxy 1102. If so, a validation statistic is updated 1303 to reflect successful recognition and operation of results decision engine 1301 is complete. Otherwise, a “filler” prompt is provided 1304 to the user, as further processing is required. For instance, the caller may be told, “Please wait, still working on it.” The specific message provided to the caller may be a default message such as this, or a more specific message provided and determined by software application through some form of reference 1101.

Processing then flows to recognition by one or more HSR resources 1305 and then a check 1306 to determine whether the HSR recognition agrees with that of the ASR. If so, statistics are again updated 1303, this time prorated because the recognition required HSR as well. In one embodiment, the proration is a deduction of ⅓ from the score that would have been provided had the confidence threshold been cleared.

If the results between the HSR and ASR recognition differ, a check 1308 is made to determine if a dual HSR was used, in which case the results from the dual HSR are used 1307 and the statistics tracking successful ASR recognition are decremented. Otherwise, an additional filler message is played 1309 and additional HSR recognition is undertaken 1310. If the HSR results do not agree, a third attempt to use HSR is performed (in some embodiments, though in others this is not done). If there is no consensus among HSRs, a “NO MATCH” result is returned, indicating none of the recognizers understands the speaker (and thus no bias is indicated for the ASR(s)). Depending on current load conditions, it may not be practical to perform second or third HSRs, in which case the single HSR result is used, again without bias to the ASR(s). In such embodiments, similar processing is used in connection with operation of results decision engines discussed in connection with FIGS. 14, 15 and 16 as well. If the ASR is determined to match the HSR recognition, processing is complete; otherwise processing flows back to 1307 to apply the HSR recognition and update the statistics as discussed above.

It should be noted that in one implementation, and ASR does not need to select from the grammar as a result of recognition; it can also return a “NO MATCH”, “NO INPUT”, or “NOISE” result, in which case further HSR processing is used as described above, again depending on criteria established by the application.

Referring now to FIG. 14, there is shown one embodiment of a results decision engine 1401, the operation of which is described as follows. Results decision engine 1401 reviews the results of recognition from two or more ASR resources and determines appropriate next steps. First, a check 1402 is made to determine whether the results from the two ASR resources agree. If they do, a check 1403 is made to determine whether the confidence is above an appropriate threshold. In one embodiment, each ASR has its own threshold, and confidence is considered sufficient if either ASR is above its confidence threshold. In that case, validation statistics are incremented 1404 for the recognizer(s) that are above threshold (with neither an increment nor a decrement in statistics for any agreeing, but below-threshold, ASR) and processing is complete.

If the results do not agree, or if the confidence level is not high enough, filler is played 1405 for the caller and HSR resources are called in 1406 to perform the recognition. Then, a check 1407 is made to determine whether at least one of the ASR results agrees with the HSR results. If not, a check 1408 is made to see whether the HSR was dual check HSR. If it was not, filler is again played 1409 and additional HSR recognition 1410 is performed. If the HSR agrees with an ASR, or if the HSR was dual check, or if a second HSR 1410 has been performed, processing moves to use the agreeing HSR results 1411, which includes decrementing statistics from disagreeing ASRs and also decrements (though at a prorated amount, ⅓ in one embodiment) statistics from any agreeing but below-threshold ASRs. Next, any agreeing above-threshold ASR validation statistics are incremented 1412, and processing is complete.

FIG. 15 illustrates processing of the results decision engine if one or more ASR resources are used with one or more HSR resources. The operation of a particular results decision engine 1501 in this instance begins by checking 1502 whether the results all agree. If so, as above a check 1503 is made to determine whether confidence for each ASR is above its threshold and if so, validation statistics are incremented 1504, with prorated deduction for any agreeing but below-threshold ASRs as discussed above. Processing is then complete.

If the results do not agree, a check 1505 is made to determine whether dual-check HSR was used and if not, filler is played 1506 while a second HSR recognition 1507 is performed. Then, the HSR results, assuming they agree, are used 1508 and statistics for disagreeing ASRs decremented as discussed above. If HSR results do not agree, processing continues as described above in connection with FIG. 13. Validation statistics are then incremented 1509, either fully or in prorated manner as previously discussed, for any agreeing ASRs. Processing is then complete.

Referring now to FIG. 16, processing of one embodiment of a results decision engine 1601 is shown in the case where solely HSR resources are used. An initial check 1602 determines whether dual-check HSR was used (assuming it was required by the calling application). If dual check was not used, filler is played 1603 and then a second HSR recognition 1604 is performed to make sure the recognition is correct.

A check 1605 is then made to determine whether the results of the HSRs are in agreement. If not, processing is complete and in one embodiment, further processing outside the scope of this process, such as a third HSR recognition (not shown), will be required to satisfy the requirements of the calling application. In such instance, if there is no convergence after the third recognition, a “no match” situation is declared indicating that recognition attempts have failed. Otherwise, the results of the at least two agreeing HSRs are used.

If the two HSR results in check 1605 do agree, then processing is complete, and the recognized utterance can, for example, be added to a group for tuning/training purposes, for instance as described above. The interpretation of responses to prompts can be viewed as two varieties of text analysis: information extraction and sense classification. Information extraction is the identification, extraction and normalization of specific fragments of information that are essential to filling the slots of business forms, such as customer IDs, telephone numbers, dates, addresses, product types, problems, and so forth. Sense classification has to do with the identification two additional types of information: meaning (intent) and response qualities. Meaning (intent) is concerned with what kind of form needs to be filled (such as, billing, scheduling an appointment, complaint, and so forth). Response quality is concerned with the response itself (such as, garbled, noise, in Spanish as opposed to English, desire to speak with a live agent and so forth).

Referring now to FIG. 17, the methods and systems discussed above can be implemented to maximize the human-like experience. As illustrated in the results of prediction optimization 1730 and media speed-up 1734, the overall recognition gap time to respond back to the application from ASR Proxy can be reduced in one example to 1.25 seconds. Going through the specific graphs in FIG. 17, 1710 represents a typical non-optimized recognition experience. The media (utterance) to recognize is 3.75 seconds long 1750. Then the ASR proxy streams the media real-time, and it typically takes some fraction of a second more to complete an automated recognition from the end of the media stream 1712. The ASR proxy results decision engine then determines that HSR (1860 in FIG. 18 as discussed below) is required, but the media (the utterance) needs to be processed from the beginning, which adds another approximately 4 seconds 1714, making the gap from a user's perspective at least 4.25 seconds. This gap can be filled in a manner by the application 1810, often called in the industry a “filler prompt” to make sure the user realizes the system is still working on the problem. This filler prompt certainly does not achieve the goal of creating a more human-like interaction with the caller. Now moving to graph 1715, the system can make improvements by speeding up the media, for example, by one second, to reduce the human assisted understanding to 3 seconds 1719 and the stop of media to recognition gap to 3.25 seconds, which is some improvement. Automated recognition provides a prediction of results, in a shorter period of time, using a partial recognition predictor as shown in 1730. As illustrated in 1732, determining that recognition has failed takes only 2 seconds, after which the ASR proxy streams the media for human assistance, speeds up the media; as a result, the overall recognition gap from end of media to successful human assistance has been significantly reduced from 4.25 seconds to 1.25 seconds, which reduces the ASR proxy recognition gap to a range that more closely matches a human-like interaction.

FIG. 18 illustrates the major system components of the ASR proxy, and elaborates certain elements of FIG. 11 to further illustrate the ASR proxy. Not part of the graphic representation of FIG. 11, but within the disclosure, is a user state management store 1813 that is expressly shown for clarity in FIG. 18. User state management 1813 has information about the user, for example, user identity, preferred channel of communication, and equipment owned. Information important to the processing of the user, such as recognition success (automation, as opposed to human assisted), is stored in the statistics store 1830 for future use. The system maintains information about the status of each interaction consisting of information, on the one hand, about the availability of intent analysts and, on the other, about the sequence of recognition requests that have been presented, the responses to these requests, the meanings (intents) of these responses, specific content extracted from these responses, and what action(s) the proxy will perform next.

The proxy processing system coordinates its actions (that is, what additional information it requests from the user and what actions it performs next with that information) based on the specific prompt, the meaning (intent) of the response to that prompt, and the specific information extracted from that response. System status subsystem 1815 keeps track of HSR capacity, or in some embodiments system load, and how it influences use of automation and human recognition. The remaining elements of FIG. 18 are as previously described with respect to the other figures, with ASR/NLU 1850 expressly shown here with multiple circles to represent multiple ASR/NLU instances as may be employed.

FIG. 19 illustrates operation of decision engines based on evaluation of system status to potentially use the capability of ASR or DTMF (as applicable based on the application). The operations are described herein as being processed by a recognition decision engine 1980 and a results decision engine 1990; those skilled in the art will recognize that various memory and processor architectures can be used to implement such engines. If there are no statistics about the recognition (1900), then automation is not used, other than to inform the application to automate using a DTMF approach if there is not enough HSR capacity. DTMF will be made available to applications that allow by business rule DTMF variation is available. In this embodiment, it would be used upon a second request for recognition from the application. In various embodiments, the application can choose to ignore the availability, and make subsequent recognition attempts, or choose to use DTMF on some recognition requests and leave the most difficult items for HSR. For example, data collection of phone number could easily be done by DTMF, while email address is more appropriately handled by HSR.

An application is in some embodiments notifies 1900 R and provides several forms of human-like interactions, depending on system status 1815 and the availability of statistics 1830: (1) human-like interactions using human assisted understanding alone 1925; (2) human-like interaction that uses a combination of automation and human assistance at high quality 1930; (3) human-like interaction that uses a combination of automation and human assistance at variable quality depending on load factor, without the requirement that the application can respond to differing quality 1930; (4) human-like interaction that uses a combination of automation 1950 and human assistance 1960 at variable quality depending on load factor 1930, with the application adjusting to lower automation confidence to increase verification prompting 1930 or 1940; and (5) interaction not intended to be human-like, such as a DTMF dialog 1940. Thus, the system responds to the capabilities of the ASR proxy and the load of the system to present different types of prompts, for example “Please press 1 for sales, press 2 for . . . ” in the case of (5) and the same question would be rephrased as “How can I help you?” illustrating the case of (1).

FIG. 20 includes the logic and components largely as described in FIG. 18 and FIG. 19, and illustrates the flow of ASR and HSR processing with statistics. Note that FIG. 21 and FIG. 22 are optional simultaneous parallel flows. FIG. 20 uses recognition decision engine 2000 and results decision engine 2020 using statistics 1820 with system status information 1815 and optionally speeding up 2010 the recognition media (voice, video) to reduce the fail-over time between automation and human assistance.

FIG. 21 illustrates an optional parallel flow where a timer statistic is combined with recognition in recognition decision engine 2100 and system status 1815. If the media is longer than typically what can be successfully recognized, which can be adjusted according to system load, then the timer event fires, and the recognition flows to human assistance 1860, with results decision engine 2150 operating as previously described.

FIG. 22 illustrates an optional parallel flow where, depending on system load prediction confidence adjustment, a recognition prediction is made on part of the media in recognition decision engine 2200, and if the recognition is not successful enough, the recognition flows to human assistance 1860, again with results decision engine 2250 operating as previously described.

FIG. 23 illustrates tuning subsystems/flows for data collection around media and media meaning to build optimal grammars and classifiers to extract meaning and to also build optimal recognition predictors. FIG. 23 describes a use case in which ASR 2310 and classifier automation are applied to a selected subset of prompts 2320 in an application. The set of application prompts fall into various categories, some of which are obvious candidates for automation, and some of which are difficult to automate. For instance, yes/no prompts and limited option prompts typically result in a very limited repertoire of user utterances and a very small number of intent labels. Relatively small amounts of data are needed to evaluate and model these types of prompt, whether for ASR grammars, statistical language models, or machine learning classifiers 2340. On the other hand, open-ended prompts, in which a user may produce a far less constrained set of utterances, are more difficult; these can be augmented by knowledge bases 2330, both general and domain-specific. Relatively larger amounts of data are needed for these types of prompts. And even when there are large amounts of data, there may still be too much diversity to produce reliable models for all types of utterances or intent labels. In other words, automation proceeds in these cases by establishing the linguistic, categorical and statistical characteristics of prompts; and driving prompt selection and development based on these characteristics. This involves a set of interrelated tasks:

-   -   the classification of utterances into different categories based         on their characteristics;     -   the identification of candidate prompts of a given application         with characteristics suitable for ASR and classifier automation;     -   determining predictors for early recognition success or failure;     -   for each prompt, developing, tuning and storing acoustic and         language models for ASR for the utterances generated by that         prompt and classifier models for the target intents of this         prompt; and     -   determining selection criteria for deciding when to exploit or         trade off ASR and classifier automation and human intent         analysts.

FIG. 24 is an example how a timer statistic could be calculated as well as a very simple predictor, using the example of breaking a North American telephone number into multiple recognition components. Elements 2401 through 2403 represent statistics gathered on successfully recognizing a specific question (prompt). Element 2401 represents a class of utterances of 2 seconds or less in length. This length represents 15% of all the utterances that have statistics in this example. ASR has been determined to be 90% successful on utterances of two seconds or less. Element 2402 represents the class of utterances of greater than 2 seconds and less than or equal to 3 seconds, with successful ASR recognition of 75%, and with 25% of utterances being in this group. Element 2403 represents a class of utterances greater than 3 seconds and less than or equal to 4 seconds. This is an example of a use case that could be influenced by system status: if there are sufficient HSR resources, a timer could be established to interrupt the recognition at 3 seconds 2402, using ASR to successfully recognize 32.3% of the utterances or, if system load increases, the timer could adjust to 4 seconds, 2403, recognizing 44.3%. Note that under very high load, ASR proxy may decide not to use a timer, though causing more wait time for the speaker, though resulting in up to 55.3% successful recognition.

Element 2404 represents the ASR recognition of a three-digit area code. Element 2405 represents the ASR recognition of a three-digit area code plus the recognition of a three-digit exchange. Element 2406 represents the ASR recognition of an entire North American telephone number. For example, if it takes approximately 8 seconds to speak the telephone number, each step 2404, 2405 and 2406 takes more time to process the utterance. The first step 2404 takes approximately 30% of the time (2.4 seconds), step two 60% of the time (4.8 seconds), and if any of the three recognition steps demonstrate results that are below confidence, then the recognition flows to human assistance. If, for example, the area code is not propertly recognized, use of HSR could happen within 2.4 seconds, while the phone number is spoken, instead of failing only after the entire phone number has been spoken.

In various embodiments and implementations, this interpretation of responses can be done either by intent analysts alone (pure HSR), by automation ASR (pure Automatic Speech Recognition and Intent Classification, or by some combination ASR and HSR. Using confidence in the results of ASR-automation to determine when ASR is generating reliable results, it is possible to trade off ASR automation for HSR with no (or a controlled) loss of quality. This means that the combination of these two approaches in the proxy processing system can achieve greater throughput than using HSR alone; and also successfully meet peak demand loads with smaller teams of intent analysts.

The subject matter above has been described in particular detail with respect to various possible embodiments. Those of skill in the art will appreciate that the subject matter may be practiced in other embodiments. First, the particular naming of the components, capitalization of terms, the attributes, data structures, or any other programming or structural aspect is not mandatory or significant, and the mechanisms that implement the subject matter or its features may have different names, formats, or protocols. Further, the system may be implemented via a combination of hardware and software, as described, or entirely in hardware elements. Also, the particular division of functionality between the various system components described herein is merely exemplary, and not mandatory; functions performed by a single system component may instead be performed by multiple components, and functions performed by multiple components may instead performed by a single component.

Some portions of above description present the features of the subject matter, process steps, and instructions in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. These operations, while described functionally or logically, could be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by real time network operating systems.

Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules or by functional names, without loss of generality.

Unless specifically stated otherwise or as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “determining” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.

The subject matter also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored on a computer readable medium that can be accessed by the computer and run by a computer processor. Such a computer program may be stored in a non-transitory computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.

In addition, the subject matter is not described with reference to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the subject matter as described herein, and any references to specific languages are provided for enablement and best mode of the subject matter.

The subject matter is well suited to a wide variety of computer network systems over numerous topologies. Within this field, the configuration and management of large networks comprise storage devices and computers that are communicatively coupled to dissimilar computers and storage devices over a network, such as the Internet.

Finally, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the subject matter. Accordingly, the disclosure herein is intended to be illustrative, but not limiting, of the scope of the subject matter. 

What is claimed is:
 1. A computer-implemented system for processing a user interaction received from a device of the user over a computer network, the interaction including text requiring recognition before being usable for further computer-implemented processing, the system comprising: an application configured to provide the text; a recognition decision engine configured to receive the text for recognition, the recognition decision engine dynamically selecting one or more recognizers from: a language model; and a human agent using a device located at a location remote from the computer-implemented system; and a results decision engine configured to provide a recognition result using the selected recognizer.
 2. The system of claim 1, further comprising a system status subsystem operably connected to the recognition decision engine, the recognition decision engine taking as input system load information from the system status subsystem for use in the dynamically selecting.
 3. The system of claim 1, wherein a subset of the one or more recognizers is configured to provide a confidence metric to the recognition decision engine, the recognition decision engine using the confidence metric in the dynamically selecting.
 4. The system of claim 3, wherein the confidence metric includes a threshold, the threshold varying based on resource availability.
 5. The system of claim 1, wherein the recognition decision engine is configured to favor selection of the language model relative to the human agent based on recognition cost factors.
 6. The system of claim 1, wherein the recognition decision engine is configured to favor selection of the language model relative to the human agent based on human resource availability factors.
 7. The system of claim 1, wherein the results decision engine is configured to update confidence thresholds associated with a first one of the recognizers responsive to agreement of results between the first one of the recognizers and a second one of the recognizers.
 8. The system of claim 1, wherein the recognition decision engine is configured to, responsive to initial results provided by the selected recognizer, make a subsequent selection of a second one of the recognizers, the subsequent selection being made before processing of the text is completed by the selected recognizer.
 9. A computer-implemented method performed by a computer-implemented system for processing a user interaction from a device of the user over a computer network, the interaction including text requiring recognition before being usable for further computer-implemented processing, the method comprising: receiving data representing the text from an application; dynamically selecting one or more recognizers from: a language model; and a human agent using a device located at a location remote from the computer-implemented system; and providing a recognition result responsive to results of processing by the selected recognizer.
 10. The computer-implemented method of claim 9, wherein said dynamically selecting is responsive to a system load metric.
 11. The computer-implemented method of claim 9, wherein said dynamically selecting is responsive to a confidence metric.
 12. The computer-implemented method of claim 9, wherein the confidence metric includes a threshold, the threshold varying based on resource availability.
 13. The computer-implemented method of claim 9, wherein said dynamically selecting favors selection of the language model relative to the human agent based on recognition cost factors.
 14. The computer-implemented method of claim 9, wherein said dynamically selecting favors selection of the language model relative to the human agent based on human resource availability factors.
 15. The computer-implemented method of claim 9, further comprising updating confidence thresholds associated with a first one of the recognizers responsive to agreement of results between the first one of the recognizers and a second one of the recognizers.
 16. The computer-implemented method of claim 9, further comprising initially choosing the language model and, responsive to initial results provided by the selected recognizer, making a subsequent selection of a second one of the recognizers, the subsequent selection being made before processing of the text is completed by the selected recognizer.
 17. A non-transitory computer-readable storage medium storing executable computer program code for processing, by a computer-implemented system, a user interaction received from a device of the user over a computer network, the interaction including text requiring recognition before being usable for further computer-implemented processing, the computer program code comprising instructions for: receiving data representing the text from an application; dynamically selecting one or more recognizers from: a language model; and a human agent using a device located at a location remote from the computer-implemented system; and providing a recognition result responsive to results of processing by the selected recognizer.
 18. The non-transitory computer-readable storage medium of claim 17, wherein said dynamically selecting is responsive to a system load metric.
 19. The non-transitory computer-readable storage medium of claim 17, wherein said dynamically selecting is responsive to a confidence metric.
 20. The non-transitory computer-readable storage medium of claim 17, wherein the confidence metric includes a threshold, the threshold varying based on resource availability. 